{"title":"检验多元平稳时间序列是高斯的","authors":"Eric Moulines, K. Choukri, M. Sharbit","doi":"10.1109/SSAP.1992.246818","DOIUrl":null,"url":null,"abstract":"These tests are based on quadratic form in deviations of certain sample statistics from their ensemble counterpart, minimised with respect to the unknown parameters. They are shown to converge under the null hypothesis to a chi-squared distribution. A specific test is developed on the basis of the difference between the sample estimate and the ensemble average characteristic functions. Preliminary results demonstrate the discriminative power of the test against various types of alternatives.<<ETX>>","PeriodicalId":309407,"journal":{"name":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Testing that a multivariate stationary time-series is Gaussian\",\"authors\":\"Eric Moulines, K. Choukri, M. Sharbit\",\"doi\":\"10.1109/SSAP.1992.246818\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"These tests are based on quadratic form in deviations of certain sample statistics from their ensemble counterpart, minimised with respect to the unknown parameters. They are shown to converge under the null hypothesis to a chi-squared distribution. A specific test is developed on the basis of the difference between the sample estimate and the ensemble average characteristic functions. Preliminary results demonstrate the discriminative power of the test against various types of alternatives.<<ETX>>\",\"PeriodicalId\":309407,\"journal\":{\"name\":\"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1992-10-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SSAP.1992.246818\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSAP.1992.246818","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Testing that a multivariate stationary time-series is Gaussian
These tests are based on quadratic form in deviations of certain sample statistics from their ensemble counterpart, minimised with respect to the unknown parameters. They are shown to converge under the null hypothesis to a chi-squared distribution. A specific test is developed on the basis of the difference between the sample estimate and the ensemble average characteristic functions. Preliminary results demonstrate the discriminative power of the test against various types of alternatives.<>